Computed tomography (CT) produces two-dimensional (2D) axial transverse tomograms which are images of body layers that are oriented essentially perpendicular to the longitudinal axis of the body. Recently, methods for three-dimensional (3D) reconstruction of image data acquired from CT scanners have been developed. Methods employing 3D reconstruction of computed tomograms to visualize blood vessels have many potential new applications in medical diagnosis. Such methods provide data sets of vascular information from a sequence of computed tomograms which represent a 3D volume.
Maximum Intensity Projection (MIP) is a commonly used technique for displaying 3D vascular image data. MIP relies on the blood in the vessel having a higher pixel intensity value than other organs of the imaged anatomy. This relationship, however, does not apply to certain types of tissues. In a CT image, for instance, the pixel intensity of bones tends to be of a higher value than that of the blood vessels. Thus, in many instances in order to correctly display the blood vessels in a 3D reconstruction of CT image data, structures having pixel intensity values similar or higher than that of blood vessels must be removed by editing.
Undesirable structures are most reliably removed using prior art manual editing methods. These methods employ an expert who manually draws outlines of the structures to be removed on every image slice using careful hand-directed cursor manipulations. The major disadvantage of such methods is that manual editing is a very repetitive process. When the number of image slices to be edited is large, as in a typical study to be 3D reconstructed using CT imaging, manual editing consumes expensive machine and operator time, notwithstanding that the operator is an expert.
Numerous interactive schemes and methods have been proposed in the prior art for helping users edit images more efficiently. One example of such a method is described in an article entitled AN IMAGE EDITOR FOR A 3D-CT RECONSTRUCTION SYSTEM by Jay Ezrielev et al. published in Proceedings of Medical Imaging IV, Image Processing, Newport Beach, 1990, Vol. 1233. The authors of this article discuss an image editing system which utilizes intelligent and semi-automated methods to improve the speed and efficiency of the editing process. Some functions are provided in their editing system which operate on entire image sets instead of individual images. These functions are capable of accomplishing thresholding operations or operations that remove simple objects from the data set. Manual editing functions are also provided to accomplish operations that the semi-automated methods are not capable of performing.
Another CT image editing method is described in an article published in IEEE Computer Graphics and Applications, November 1991, entitled EDITING TOOLS FOR 3D MEDICAL IMAGING by Derek R. Ney et al. In this article, the authors present an editing method which is patterned after a paint and drawing program. This editing method lets the user interactively create shapes manually which are used to define volumes of interest to be edited in images of medical data.
These and other methods, however, are still not capable of relieving the user from the time consuming and tedious editing process of manually drawing regions of interest to be edited from the image slices. More recently, however, a quick and user-friendly interactive editing method has been developed which facilitates the process of removing undesirable structures from image slices used in 3D reconstruction. This editing method makes use of the ability to view several consecutive images as one superimposed image. This method generally involves defining the desired modifications on the superimposed image and then applying these modifications to the individual images slices. This saves much time and effort by avoiding the need to individually edit each image.
Specifically, this method involves modifying a stack of images to remove certain tissues such as bones and uses the modified images for the 3D visualization of blood vessels. The stack of images is subdivided into a number of subsets or slabs. For each slab, a superimposed image is computed by applying an MIP algorithm to the image data of the slab. The computed superimposed image is commonly referred to as a top MIP image. The user manually makes modifications to the top MIP image of every slab. These modifications are then applied to every image in the slab from which the top MIP image is derived.
The top MIP image can be edited in a number of ways. A user can manually draw contours around the regions to be removed or retained. The user can also edit the individual images to supplement the modifications made to the top MIP image. All the slabs of the stack are sequentially traversed during the editing process, such that the current slab inherits the contours of the previously modified slab. Thus, the user can adjust the inherited contours on the top MIP image of the third slab instead of having to draw them from scratch.
Although the prior art editing method described above is a very significant improvement over earlier editing methods, it still requires the user to manually outline regions of interest by hand. The continuing necessity to perform manual editing in the aforementioned prior art method ultimately limits the speed of the editing process. Hence, there remains a need for further improvements in the speed of the editing for 3D reconstruction imaging.
It is, therefore, a primary object of the present invention to increase the editing speed of abdominal CT angiography images used for 3D blood vessel visualization. This is accomplished by providing a novel editing method for automatically outlining regions to be removed from the CT images.